• Online Text R for Data Science by Garrett Grolemund & Hadley Wickham
  R for Data Science Ch. 7 - EDA

• The Hass Avocado Board and Justin Kiggins for compiling Avacado data
  Dataset csv download link on Kaggle

• Data Visualization published in Nature Methods
  Nature.com Blog

• DataCamp Course by Rick Scavetta: Data Visualization with ggplot

EDA is a creative process with the purpose of exploring your data to generate quality questions. It is not quantitative but more qualitative in nature and is iterative as the exploration inspires questions which become more exploration and so on.

“There are no routine statistical questions, only questionable statistical routines.” — Sir David Cox

A good starting place for questions:

• What type of variation occurs within my variables?
  
• What type of covariation occurs between my variables?

To answer these question its crucial to know if our data is Categorical or Numerical.

## # A tibble: 4 x 6
##   set   `mean(x)` `sd(x)` `mean(y)` `sd(y)` `cor(x, y)`
##   <fct>     <dbl>   <dbl>     <dbl>   <dbl>       <dbl>
## 1 I             9    3.32      7.50    2.03       0.816
## 2 II            9    3.32      7.50    2.03       0.816
## 3 III           9    3.32      7.5     2.03       0.816
## 4 IV            9    3.32      7.50    2.03       0.817

Finding Data

• Google Dataset Tool Search
• Kaggle Datasets
• Awesome-Public-Dataset Repo
• Built in R Datasets or Dataset packages on CRAN. To see the list type data() in the console.

Using geom_jitter() and geom_quasirandom() with the data set filtered to the region New York.

So far we primarily have been using position mapping and/or color mapping. But we are able to map more variables to the following aesthetics: